Pricing and advertising for reward-based crowdfunding products in E-commerce

https://doi.org/10.1016/j.dss.2019.113231Get rights and content

Highlights

  • A creator collects money through crowdfunding, and then invests in advertising.

  • Equilibrium decisions are influenced by potential demand and the gap of valuations.

  • The creator prefers a low crowdfunding price (L or M policy) for a big end market.

  • The creator may never choose the L policy if the valuation gap is high enough.

Abstract

This paper studies a reward-based crowdfunding game wherein the creator first collects money through an E-crowdfunding platform and then, if successful, invests the raised money to fund promotional activities via online retailing. We derive the creator's equilibrium decisions and the corresponding payoff and show that they are significantly influenced by the potential demand and the gap between different types of buyer valuations. In particular, with the change in potential market size from low to high, the creator will choose investing none, part or all of the crowdfunded money to improve the advertising level. Meanwhile, when the market size is small, the creator tends to set a high price; when it becomes larger, the creator prefers menu prices or a low price to guarantee the success of the crowdfunding campaign. Comparing two scenarios, one with and one without considering advertising investment, creators may never choose the low price if they cannot raise enough money for advertising; however, that situation can never arise without considering the option of advertising investment. These results provide implications for launching a successful crowdfunding campaign and product promotion/advertising in this E-commerce era.

Introduction

Reward-based crowdfunding refers to a business innovation mode that a firm uses to raise money for its new products via an online platform to support its production and advertising activities. With the rapid development of Internet technology and the prosperity of E-commerce, reward-based crowdfunding has already become a new star in online retailing, attracting great attention for its importance in raising sufficient financial resources in various forms, such as donations, or in exchange for other rewards [2]. For example, from its launch in 2009 until January 2018, “Kickstarter”, the biggest E-crowdfunding platform in the world, successfully collected $3.5 billion and funded 139,169 projects.1 Reward-based crowdfunding has become increasingly popular [7,27] and been applied by many crowdfunding platforms, e.g., Indiegogo and Kickstarter. We use an online movie project to illustrate the typical process of a reward-based crowdfunding project and its subsequent selling activities.2

A movie design company first launches a movie trailer (or an idea) on an E-crowdfunding platform (e.g., http://Kickstarter.com) with a presale price, funding target and deadline. We call this movie design company the creator in such crowdfunding processes. Potential consumers surfing the crowdfunding platform can review this movie project and decide whether to make a pledge once they are interested in the project. We call these consumers the buyers. When the deadline approaches, if the funded amount reaches or exceeds the target, the creator receives all the raised money to support its filming, and all the buyers who have pledged funds will obtain digital copies of the movie afterwards. Otherwise, if the funded amount is below the target, the project fails and all the money will be refunded to the pledged buyers via online channels. More importantly, if the crowdfunding campaign succeeds, the creator can consider investing the crowdfunded money in advertising to promote the new movie, of which digital copies or DVDs will be sold via another E-business platform. For example, Amazon has had a Launchpad subsection since July 2016 to showcase products that were originally crowdfunded on the Kickstarter platform.3

Based on the above discussion, one can see that a standard online crowdfunding project normally consists of two periods: the crowdfunding stage and the regular (online) selling stage. In the crowdfunding stage, the creator has to determine a proper crowdfunding price to achieve a balance between raising enough money and promising a relatively higher success rate in the crowdfunding stage. After that, if the campaign succeeds, the creator will decide whether to invest some of the crowdfunded money in advertising in the regular selling stage and how much of it to invest. It is quite common to see that in practice, firms invest their crowdfunded money in the production and promotion process to further promote their products. One typical example is in the film market. Big Fish & Begonia4 has been one of the most successful crowdfunding film projects in China, spending nearly $6 million for production and $4 million for dispersion and advertising. It finally took in nearly $80 million at the box office.5 Building upon these characteristics, this paper studies how crowdfunding facilitates funding raising and new product promotion in these online retailing activities. In particular, we are interested in the following questions:

  • What kind of crowdfunding pricing policy should the creator adopt with an overall consideration of both crowdfunding and regular selling, and how much crowdfunded money should the creator invest in advertising?

  • Under alternative pricing policies, how do the equilibrium payoffs vary under various circumstances, especially for different potential demands and buyers' product valuations and fractions?

  • How will the creator's best choices for crowdfunding pricing policies be different between two scenarios, i.e., either considering or not considering advertising investment in the regular selling stage?

To answer the above questions, we build a stylized two-stage decision model for the creator to determine the pricing policy in the crowdfunding stage and the advertising level in the regular selling stage in that order. In particular, buyers on the E-crowdfunding platform make prepurchase decisions according to price, private valuations and the anticipated success rate. Notably, if a buyer pays a higher price, that buyer makes a greater contribution to the success rate. In this sense, buyers with different product valuations could pay different prices even for the same identical product. However, the buyers arriving at the regular selling stage only care about the price and their private valuations of the product. Therefore, the creator pays more attention to the total number of buyers who come in the regular selling stage, which can be potentially influenced by the advertising in the E-platform. We adopt the backward induction method to derive the crowdfunding pricing policies and the advertising levels in equilibrium as well as the creator's corresponding payoffs.

Our conclusions shed light on the creator's best decisions about the crowdfunding pricing policies when facing different buyers and market circumstances. Intuitively, if foreseeing a small-end market in the regular selling stage, the creator will prefer a high crowdfunding price (H policy) in order to raise more targeted money, even enduring the risk of the failure of the crowdfunding campaign and thus the subsequent selling activities. On the contrary, if foreseeing a large end market, the creator will face a trade-off between setting a lower price (L policy) to guarantee a higher success rate for the crowdfunding campaign and setting a higher price (M policy) to raise more targeted money for the advertising investment in the regular selling stage. According to our result, the difference in product valuations between two types of buyers plays a pivotal role in the creator's choice in this dilemma. If the gap between the buyers' valuations is high, which means that the two types of valuations differ significantly, the creator will provide menu crowdfunding prices to screen different types of buyers with a higher target. Otherwise, if the gap between the buyers' valuations is low, the creator will first prefer the M policy and then the L policy as the market increases, indicating that the success rate becomes an increasingly important concern.

We also show a series of interesting trends for equilibrium advertising levels and the payoffs varying with the potential market size, regardless of which pricing policy the creator adopts. Specifically, when the potential market size is very large, the creator is willing to invest the full amount of the crowdfunded money in advertising to improve the end market size; otherwise, if the market potential is very small, the creator will be reluctant to invest more in advertising. When the potential market size is medium large, the creator will only invest part of the crowdfunded money in advertising, and the investment level increases with the potential market size. Moreover, when the market size is medium large and keeps increasing, the creator's payoff will exhibit a dramatic increase with the market potential, whose level is double that of the advertising level.

Our model also examines the impact of advertising investment on the creator's pricing choice in crowdfunding. To this end, we compare the creator's equilibrium strategies and payoffs under two representative scenarios: with and without future investment in advertising. We show that under either scenario, the creator tends to set a high price only when the market potential is small but prefers lower prices (e.g., M or L policies) when the market potential becomes larger in order to guarantee the success of the crowdfunding campaign. Nevertheless, if giving consideration to advertising investment, the creator may never choose a low price (L policy) if the gap between the two types of buyers' valuations is large enough. This result can never arise if the creator cannot invest in advertising in the regular selling stage.

The remainder of this article is organized as follows. Section 2 reviews the relevant literature. In Section 3, we lay out the model setup. The analysis of the creator's equilibrium decisions on prices and advertising investment are presented in Section 4. Section 5 investigates the creator's best choices for the crowdfunding prices under two different scenarios. Section 6 presents extensions from various perspectives, and Section 7 concludes the article. Proofs are presented in Appendix A.

Section snippets

Related literature

This research is closely related to two streams of literature: reward-based crowdfunding and advertising investment. In general, crowdfunding can be classified into four categories, depending on the return type offered for the funders' support [7, 12] : donation-based, reward-based, lending and equity. One of the most prevalent types is reward-based crowdfunding, which incurs an exchange of a monetary contribution from supporters for some kinds of non-equity rewards from creators or initiators [

Model

Consider a risk-neutral creator who raises money via a crowdfunding campaign to support the subsequent promotion activities of the new product. We build a two-stage model consisting of the crowdfunding stage (t = 1) and the regular selling stage (t = 2), wherein each buyer makes a purchase decision according to his or her private valuation and the price(s) of the product. The buyers have different tastes or preferences regarding the product, which are captured by their valuations. In line with

Equilibrium advertising investments and payoffs

First, we analyze the equilibrium advertising levels at which the creator invests to reach alternative pricing policies. After that, we identify the combinations of pricing policies in the two stages and then present the expected ex-ante payoffs in equilibrium accordingly.

Optimal pricing policy

In this section, we discuss the creator's best pricing policies for crowdfunding based on the ex-ante payoffs and examine comparisons from various perspectives. For ease of exposition, we let ĥ=hc and l^=lc, and replace all the results shown in Table 1 correspondingly. Meanwhile, we assume α=12 to simplify the comparative analysis. In the next section, we will conduct a sensitivity analysis of parameters α and c.

Price commitment

We have investigated the creator's choice among three alternative pricing policies (i.e., the H policy, M policy and L policy) in the crowdfunding stage, in which case the creator could raise the regular selling price. In contrast, in this subsection, we consider a new scenario in which the pricing policy cannot be changed over two stages, indicating that a price commitment between crowdfunding and regular selling is made once the crowdfunding price is determined. Consequently, there are only

Conclusion

Reward-based crowdfunding platforms have helped a substantial number of new product designers raise sufficient money from consumers to support the production and selling activities of these new products. The typical process of crowdfunding begins with a creator launching a presale crowdfunding campaign to the buyers and then promotes the new product once the campaign succeeds. This paper studies the creator's optimal decisions regarding crowdfunding prices and advertising investments, which are

Acknowledgments

This work was supported by the National Natural Science Foundation of China (71971052,71671033,71922010,71871167,71620107003), the Fundamental Research Funds for the Central Universities (N160601001), the Fund for Innovative Research Groups of the National Natural Science Foundation of China (71621061,71821001, 71472069), the Project of Promoting Talents in Liaoning Province (XLYC1807252) and the 111 Project (B16009).

Xu Guan received his Ph.D. degree in Management Science and Engineering, from Huazhong University of Science and Technology in 2013. He is currently a professor in the School of Management at Huazhong University of Science and Technology, Wuhan. His current research interests include operations-marketing interface, and supply chain management. His work has appeared in several leading conferences and journals in the fields of operations research like Production and Operations Management, Journal

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    Xu Guan received his Ph.D. degree in Management Science and Engineering, from Huazhong University of Science and Technology in 2013. He is currently a professor in the School of Management at Huazhong University of Science and Technology, Wuhan. His current research interests include operations-marketing interface, and supply chain management. His work has appeared in several leading conferences and journals in the fields of operations research like Production and Operations Management, Journal of Retailing, Decision Sciences, Naval Research Logistics and others.

    Wan-Jiang Deng received the B.S. degree in Logistics Management from Huazhong University of Science and Technology, Wuhan, China, in 2014. He is currently pursuing the Ph.D. degree in Management Science and Engineering at Huazhong University of Science and Technology. His research interests include crowdfunding and operations-marketing interface.

    Zhong-Zhong Jiang received the B.S. and M.S. degrees in Physics and the Ph.D. degree in Systems Engineering from Northeastern University, Shenyang, China, in 2000, 2003, and 2006, respectively. He is currently a professor and the Dean with the School of Business Administration, and the Director of the Institute of Behavioral and Service Operations Management, Northeastern University, Shenyang, China. He was a senior visiting scholar with the Hong Kong Polytechnic University, North Carolina State University, and University of Minnesota, in 2008, 2010-2011, and 2015, respectively. His current research interests include e-commerce and sharing economy, behavioral operations and revenue management, logistics and supply chain optimization, and service operations management. He has published academic papers in many journals, such as European Journal of Operational Research, Transportation Research Part-B, IEEE Transactions on Fuzzy Systems, IEEE Systems Journal, and Computers & Industrial Engineering.

    Min Huang received the B.S. in Automatic Meters and Measurements, M.S. degrees in Systems Engineering and the Ph.D. degree in Control Theory and Control Engineering from Northeastern University, Shenyang, China, in 1990, 1993, and 1998, respectively. She is currently a professor in the College of Information Science and Engineering at Northeastern University Shenyang, China, and was a senior visiting scholar in the Department of Industrial and Operations Engineering at University of Michigan (Ann Arbor) in 2011. She is the head of the Department of Artificial Intelligence, the director of the Institute for Data Intelligence & Systems Engineering, and director of International Research Center of Intelligent Analysis and Decision Making for Future Manufacturing and Services. Dr. Huang has been recognized as the Distinguished Young by the National Science Foundation of China and Changjiang Scholarship Chair Professor of MOE of China. Her research interests cover the modeling, analytics and optimization for manufacturing systems, logistics and supply chain systems, risk management, behavioral operations management, computational intelligent etc. She has been showered with eight prizes and awards honored by the state or MOE of China. She has authored more than 120 refereed publications in international academic journals, such as Omega, EJOR, TRB, and C&OR. She currently serves as the associate editor of Asia-Pacific Journal of Operational Research and a member of the editorial board of Asian Journal of Management Science and Applications.

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